In our last post, we discussed considerations for using chat/collaboration apps within the workplace. Some of those considerations influence how discovery is conducted from those chat/collaboration apps.
Not only that, but there are additional considerations for discovery of evidence from those apps. Let’s look at five of those considerations and four best practices for the discovery of data from chat/collaboration apps.
Five Considerations for Chat/Collaboration App Discovery
When it comes to discovery of evidence from chat/collaboration apps, considerations are all about the data. These considerations include:
Finding the Data
As is the case with most platforms used for communication or work product generation, the search capabilities for most collaboration apps are somewhat limited for discovery, making it difficult to pinpoint specific communications and content that may be responsive to the case. As a result, you may need to collect more broadly to ensure your collection is as inclusive of potentially responsive evidence as possible.
Collecting the Data
Once you’ve identified data that is potentially responsive, collecting that data is often difficult, as the location(s) and way the data is stored can vary widely. For example, Microsoft Teams stores chat messages within Exchange, shared files within OneDrive for Business, images and emoji within Azure Media Services and meeting recordings can be stored within OneDrive for Business or the Teams SharePoint site (depending on whether they’re private or channel meeting recordings.) That’s multiple locations from which data collection must occur. Not to mention, the practice of embedding links to files (often called “modern attachments”) instead of embedding the file itself adds complexity for many chat/collaboration apps today.
Converting the Data
Presenting the Data
Another consideration with chat/collaboration apps is how the data will be presented to illustrate conversations. Unlike emails where every email is a snapshot of the conversation up to that point, chat messages are individually stored, requiring the conversations to be pieced together in a logical manner – often by day or through the entire exchange between the parties. If that last sentence sounds familiar, it’s because the same challenge exists when discovering text messages!
Repeating the Process for Each Type of Data
Now take all those considerations and multiply them. That’s because – unlike mobile devices, where there are primarily two platform choices (Android and iOS) – most organizations use multiple (if not many) chat/collaboration apps, each with their own unique way of storing data. Our last post illustrated the challenge here with so many different types of apps to choose from and the shadow IT issue, which means that they tend to proliferate within organizations, forcing your discovery team to create a plan for finding, collecting, converting, and presenting the data for each – in a manner that facilitates review and production for discovery.
Four Best Practices for Chat/Collaboration App Discovery
How do you address all the considerations discussed above? Here are four best practices your team should keep in mind when discovering data from chat/collaboration apps:
Develop a Plan Up Front
To start with, you need to develop a plan up front for how discovery will be conducted for each chat/collaboration app. Each plan should address the nuances associated with finding, collecting, converting and presenting the data in discovery and should be tested to confirm viability. Having a tested, documented plan will also make your process more defensible if there are discovery disputes. As we’ll discuss in our next post, case law has shown that courts will expect parties to produce data from these apps in discovery – and penalize them severely when they fail to do so.
Extend Planning to ESI Protocols
That up-front planning should include verbiage in your ESI protocol templates that discusses how discovery will be conducted from these apps (when applicable). You want to dictate how discovery will be performed from these apps when you produce data from them, not have your opposing party do so because you failed to address them in the ESI protocol.
Find the Tech That’s Right for You
Many eDiscovery platforms help automate the discovery process for chat and collaboration apps – especially Slack, which offers a Discovery API to facilitate that process. However, they don’t necessarily handle the same apps or present the data the same way in each app. Find the platform that is best suited for the chat/collaboration apps your organization uses and demand a free trial of any platform you’re considering.
Enlist the Help of an Expert
Developing a plan for each chat/collaboration app and finding the platform best suited to your needs isn’t a task suited for novices. Enlist an expert experienced in both discovery best practices and the pros and cons of different eDiscovery platforms to help create the plans and select the best platform for your organization. An expert will help your organization minimize the ramp-up time to get your process and solution in place, while helping you to avoid the mistakes that many other organizations have made.
If an app potentially involves business communication and work product generation, data from that app is potentially discoverable. Now is the time to plan for how discovery will be conducted from that app – not when your organization is hit with a discovery request.
Not only that, but you will also need to revisit chat/collaboration app discovery planning regularly as organizations are using new apps all the time. Standing still is falling behind when it comes to discovery of chat/collaboration apps.
Next time, we will look at recent case law involving chat/collaboration app discovery, including mistakes made by several other organizations.
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